src/configs/refinement_cfg_train_withvertexwisegc_isflat_csmorestanding.yaml

paths:
  ROOT_OUT_PATH: './results/'
  ROOT_CHECKPOINT_PATH: './checkpoint/'
  MODELPATH_NORMFLOW: './checkpoint/barc_normflow_pret/rgbddog_v3_model.pt'

smal:
  SMAL_MODEL_TYPE: '39dogs_norm_newv3'    # '39dogs_norm'  # '39dogs_diffsize'  # 'barc'
  SMAL_KEYP_CONF: 'olive'   # 'green'

optim:
  LR: 5e-5    # 5e-7  # (new) 5e-6   # 5e-5    #  5e-5  # 5e-4
  SCHEDULE: [150, 175, 200]   # [220, 270]   # [150, 175, 200]
  GAMMA: 0.1
  MOMENTUM: 0
  WEIGHT_DECAY: 0
  EPOCHS: 220   # 300
  BATCH_SIZE: 14  # 12       # keep 12 (needs to be an even number, as we have a custom data sampler)
  TRAIN_PARTS: 'refinement_model'   # 'refinement_model_and_shape' # 'refinement_model'

params:
  REF_NET_TYPE: 'multrot01all_res34' # 'multrot01all_res34'    # 'multrot01all'    # 'multrot01'   # 'multrot01'   # 'multrot01'  # 'multrot'   # 'multrot_res34' # 'multrot' # 'add'
  REF_DETACH_SHAPE: True
  GRAPHCNN_TYPE: 'multistage_simple'  # 'inexistent'
  SHAPEREF_TYPE: 'inexistent'   # 'linear'  # 'inexistent'
  ISFLAT_TYPE: 'linear'     # 'inexistent'  # 'inexistent'

data:
  DATASET: 'stanext24_withgc_csaddnonflatmorestanding'  #  'stanext24_withgc_csaddnonflat'  # 'stanext24_withgc_cs0'
  SHORTEN_VAL_DATASET_TO: 600  # this is faster as we do not evaluate on the whole validation set  
  VAL_OPT: 'val'